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---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-sentence-classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-sentence-classifier
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6266
- Accuracy: 0.7990
- Macro F1: 0.7614
- Micro F1: 0.7990
- Qwk: 0.6588
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Micro F1 | Qwk |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:--------:|:--------:|:------:|
| 0.6267 | 1.0 | 27540 | 0.6108 | 0.7818 | 0.7364 | 0.7818 | 0.6352 |
| 0.5539 | 2.0 | 55080 | 0.5939 | 0.7911 | 0.7498 | 0.7911 | 0.6428 |
| 0.475 | 3.0 | 82620 | 0.6021 | 0.7977 | 0.7592 | 0.7977 | 0.6599 |
| 0.4204 | 4.0 | 110160 | 0.6266 | 0.7990 | 0.7614 | 0.7990 | 0.6588 |
### Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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